The process of 骨架提取, or skeleton extraction, involves several key steps. Please note that this response will provide a general overview of the process, and the specific implementation may vary depending on the algorithm used. Here are the main steps involved:

  1. Preprocessing: This step involves performing any necessary preprocessing on the input image or data. This may include resizing, smoothing, or enhancing the image to improve the quality of the results.

  2. Binarization: The next step is to convert the preprocessed image into a binary image, where each pixel is assigned either a foreground (object) or background (non-object) value. This can be achieved through thresholding or other binarization techniques.

  3. Thinning: Thinning is a crucial step in skeleton extraction. It aims to reduce the foreground objects to their skeleton representation while preserving the connectivity and topology of the original shape. Various thinning algorithms, such as Zhang-Suen, Guo-Hall, or iterative morphological operations, can be used to achieve this.

  4. Pruning: After thinning, the skeleton may contain extraneous branches or noise. Pruning removes these artifacts by applying rules based on length, connectivity, or other characteristics to refine the skeleton further.

  5. Post-processing: The final step involves any additional post-processing operations to refine the skeleton further or enhance its quality. This may include smoothing, branch point identification, or feature extraction to analyze and interpret the skeleton's information.

  It is worth noting that skeleton extraction is a complex field with numerous algorithms and techniques available. The specific steps and algorithms used can vary depending on the application, data type, and desired outcomes. Researchers and developers continue to explore and develop new approaches to improve skeleton extraction in different domains.